Systems and methods for diagnosis and assessment of cardiovascular disease by comparing arterial supply capacity to end-organ demand

ABSTRACT

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient&#39;s heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

RELATED APPLICATION(S)

This application claims priority to U.S. Provisional Application No.62/236,707 filed Oct. 2, 2015, the entire disclosure of which is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

Various embodiments of the present disclosure relate generally todisease assessment, treatment planning, and related methods. Morespecifically, particular embodiments of the present disclosure relate tosystems and methods for assessing cardiovascular disease by comparingarterial supply capacity to end-organ demand.

BACKGROUND

Coronary artery disease is a common ailment that affects millions ofpeople. Coronary artery disease may cause the blood vessels providingblood to the heart to develop lesions, such as a stenosis (abnormalnarrowing of a blood vessel). As a result, blood flow to the heart maybe restricted. A patient suffering from coronary artery disease mayexperience chest pain, referred to as chronic stable angina, duringphysical exertion or unstable angina when the patient is at rest. A moresevere manifestation of disease may lead to myocardial infarction, orheart attack. Significant strides have been made in the treatment ofcoronary artery disease including both medical therapy (e.g. statins) orsurgical alternatives (e.g., percutaneous coronary intervention (PCI)and coronary artery bypass graft surgery (CABG)). Invasive assessmentsare commonly used to assess the type of treatment a patient may receive.However, indirect or noninvasive assessments for formulating a patienttreatment are being explored and developed.

Heart disease is typically viewed as resulting from vessel disease, inparticular, narrowing or blockage inside vessel lumens in a way thatimpacts blood flow. Currently, treatment assessment takes into accountsuch intraluminal factors. However, a desire exists to improve thediagnosis and/or treatment of cardiovascular disease by better assessingthe severity of disease.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of thedisclosure.

SUMMARY

According to certain aspects of the present disclosure, systems andmethods are disclosed for using a relationship between arterial bloodsupply and organ or tissue demand to guide diagnosis or treatment ofcardiovascular disease.

Systems and methods are disclosed for to determining a blood supply andblood demand. One method and/or system includes steps of receiving apatient-specific model of vessel geometry of at least a portion of acoronary artery, wherein the model may be based on patient-specificimage data of at least a portion of a patient's heart having myocardium;determining a coronary blood supply based on the patient-specific model;determining at least a portion of the myocardium receiving blood fromthe coronary artery; determining a myocardial blood demand based oneither a mass or a volume of the portion of the myocardium, or based onperfusion imaging of the portion of the myocardium; and determining arelationship between the coronary blood supply and the myocardial blooddemand.

Other methods and systems may further comprise evaluating the patientbased upon the determined relationship between the coronary blood supplyand the myocardial blood demand.

Other methods and systems may further comprise determining whether amismatch exists between the coronary blood supply and the myocardialblood demand based on the determined relationship between the coronaryblood supply and the myocardial blood demand.

Other methods and systems may further comprise, based on thedetermination of whether the mismatch exists, modifying at least oneparameter of a patient-specific simulation of blood flow through atleast the portion of the coronary artery.

Other methods and systems may further comprise comparing therelationship to a reference value.

Other methods and systems may determine the reference value from apopulation of patients.

Other methods and systems may further comprise receiving a secondpatient-specific model representing coronary arterial vasculaturedownstream from the portion of the coronary artery.

In methods and systems herein, the mass of the portion of the myocardiummay be calculated by measuring or assuming a tissue density of theportion of the myocardium, and multiplying the tissue density by thevolume of the portion of the myocardium.

In methods and systems herein, the patient-specific model may begenerated by modifying a generic model of vessel geometry.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the disclosed embodiments, as claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments,and together with the description, serve to explain the principles ofthe disclosed embodiments.

FIG. 1 is a block diagram of an exemplary system and network forassessing a patient based on analysis of blood supply and organ ortissue demand, according to an exemplary embodiment of the presentdisclosure.

FIGS. 2A and 2B are images of arteries of patients obtained using animaging device.

FIG. 3 is a block diagram of an exemplary method of determining arelationship between coronary blood supply and organ or tissue blooddemand, according to an exemplary embodiment of the present disclosure.

FIG. 4 is a block diagram of an exemplary method of comparing apatient-specific relationship between arterial blood supply and organ ortissue blood demand to that of a population of prior patients, accordingto an exemplary embodiment of the present disclosure.

FIG. 5 is a block diagram of an exemplary method of determining arelationship between arterial blood supply and organ or tissue blooddemand, according to an exemplary embodiment of the present disclosure.

FIG. 6 is a block diagram of an exemplary method of determining arelationship between arterial blood supply and organ or tissue blooddemand to update a simulation of blood flow and pressure, according toan exemplary embodiment of the present disclosure.

FIG. 7 is a block diagram of an exemplary method of determining acoronary supply and myocardial demand, according to an exemplaryembodiment of the present disclosure.

DESCRIPTION OF THE EMBODIMENTS

Reference will now be made in detail to the exemplary embodiments of thedisclosure, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like parts.

Coronary artery disease is a common ailment, by which blood flow to theheart may be restricted. While significant strides have been made in thetreatment of coronary artery disease, the treatment is often misplacedor excessive. For example, patients often undergo invasive surgicaltreatments when medication may suffice. Patients are sometimes subjectedto treatments that may not change their condition. In some situations,patients even undergo treatments that ultimately worsen their condition.Thus, a need exists to accurately assess the severity of cardiovasculardisease in selecting a course of treatment.

When assessing cardiovascular disease, and diseases of other organs andtissues in a patient, it is believed that for healthy individuals, thecaliber of the arterial tree is sized appropriately to meet the demandsof the tissue and organ supplied. For example, in the coronary arterialtree, large arteries on the epicardial surface of the heart, theepicardial coronary arteries, are assumed to conduct flow to the heartmuscle (myocardium) through the smaller arteries, arterioles andcapillaries with only minimal resistance to flow and, as a result, smallgradients in pressure. Moreover, it is generally assumed that myocardialischemia, a lack of blood flow to the muscle of the heart, is caused byeither focal or diffuse atherosclerosis in the epicardial coronaryarteries or microvascular dysfunction, i.e., an inability of themicrocirculation to dilate in response to an increased demand for flow.These assumptions on the idealized relationship between myocardialtissue demand and the supply capacity of the epicardial coronaryarteries, has led to a focus on diagnosing coronary artery disease oneither the presence of obstructive anatomic disease in the coronaryarteries using invasive coronary angiography (ICA), IntravascularUltrasound (IVUS), invasive Fractional Flow Reserve (FFR), coronarycomputed tomography angiography (CCTA), noninvasive Fractional FlowReserve derived from CT (FFR_(CT)), or on functionally significantdisease assessed using myocardial perfusion imaging (MPI) using SinglePhoton Computed Emission Tomography (SPECT), Positron EmissionTomography (PET), Magnetic Resonance Perfusion Imaging (MRMPI), orComputed Tomography Perfusion imaging (CTP).

There has been a lack of understanding of and diagnostic methods toexamine the relationship between the supply capacity of the coronaryarteries and the end-organ demand of the myocardial muscle. There aremany patients that present to the emergency department or their primarycare doctors or cardiologists complaining of symptoms suggestive ofcoronary artery disease that, upon testing, have normal ICA and CCTAanatomic tests, but abnormal functional tests. Furthermore, there is agap in functional testing, whereby methods to examine epicardialdisease, such as FFR and FFR_(CT), are generally ordered only when thereis evidence of obstructive coronary artery disease narrowing the bloodvessel. As a result, some patients that have symptoms of heart diseasereceive an improper or inadequate diagnosis as a result of the lack of amethod to examine the relationship between coronary supply andmyocardial demand. Novel techniques presented herein may be used to moreaccurately diagnose artery disease by analyzing the relationship betweencoronary supply and myocardial demand.

Referring now to the figures, FIG. 1 depicts a block diagram of anexemplary system 100 and network for assessing a patient based onanalysis of blood supply and organ or tissue demand, according to anexemplary embodiment. Specifically, FIG. 1 depicts a plurality ofphysicians 102 and third party providers 104, any of whom may beconnected to an electronic network 101, such as the Internet, throughone or more computers, servers, and/or handheld mobile devices.Physicians 102 and/or third party providers 104 may create or otherwiseobtain images of one or more patients' anatomy. The physicians 102and/or third party providers 104 may also obtain any combination ofpatient-specific information, such as age, medical history, bloodpressure, blood viscosity, patient activity or exercise level, etc.Physicians 102 and/or third party providers 104 may transmit theanatomical images and/or patient-specific information to server systems106 over the electronic network 101. Server systems 106 may includestorage devices for storing images and data received from physicians 102and/or third party providers 104. Server systems 106 may also includeprocessing devices for processing images and data stored in the storagedevices.

FIGS. 2A and 2B are images of coronary arteries obtained from patientsusing an imaging device. The image data of FIGS. 2A and 2B may becaptured, processed, and/or stored by server systems 106. The image datamay be based on information, images, and/or data received fromphysicians 102 and/or third party providers 104 over electronic network101.

As shown in FIGS. 2A and 2B, anatomic data may be obtained noninvasivelyusing, for example, coronary computed tomographic angiography (CCTA).CCTA may be used for imaging of patients with chest pain and involvesusing computed tomography (CT) technology to image the heart and thecoronary arteries following an intravenous infusion of a contrast agent.However, CCTA also cannot provide direct information on the functionalsignificance of coronary lesions, e.g., whether the lesions affect bloodflow. In addition, since CCTA is purely a diagnostic test, it cannot beused to predict changes in coronary blood flow, pressure, or myocardialperfusion under other physiologic states, e.g., exercise, nor can it beused to predict outcomes of interventions.

Thus, patients may also require an invasive test, such as diagnosticcardiac catheterization, to visualize coronary lesions. Diagnosticcardiac catheterization may include performing conventional coronaryangiography (CCA) to gather anatomic data on coronary lesions byproviding a doctor with an image of the size and shape of the arteries.CCA, however, does not provide data for assessing the functionalsignificance of coronary lesions. For example, a doctor may not be ableto diagnose whether a coronary lesion is harmful without determiningwhether the lesion is functionally significant. Rather, a doctor mayinsert a stent because, as shown in FIG. 2B, a portion of an arteryappears that it has a substantial degree of stenosis (DS), for example,the degree of stenosis is greater than 50% of the vessel lumen. Thus,CCA has led to what has been referred to as an “oculostenotic reflex” ofsome interventional cardiologists to insert a stent for every lesionfound with CCA regardless of whether the lesion is functionallysignificant. As a result, CCA may lead to unnecessary operations on thepatient, which may pose added risks to patients and may result inunnecessary heath care costs for patients. Techniques presented hereinmay remedy one or more of these problems by determining a relationshipbetween blood supply and organ or tissue demand.

FIG. 3 is a block diagram of an exemplary method of determining arelationship between coronary blood supply and organ or tissue blooddemand. FIG. 4 is a block diagram of an exemplary method of comparing apatient-specific relationship between arterial blood supply and organ ortissue blood demand to that of a population of prior patients. FIG. 5 isa block diagram of an exemplary method of determining a relationshipbetween arterial blood supply and organ or tissue blood demand. FIG. 6is a block diagram of an exemplary method of determining a relationshipbetween arterial blood supply and organ or tissue blood demand to updatea simulation of blood flow and pressure. FIG. 7 is a block diagram of anexemplary method of determining a coronary supply and myocardial demand.

In contrast with conventional techniques, embodiments of the presentdisclosure may determine a relationship between arterial blood supplyand organ or tissue blood demand in order to more accurately assessvascular health. FIG. 3 is a block diagram of an exemplary method ofdetermining a relationship between coronary blood supply and myocardialblood demand, according to an exemplary embodiment of the presentdisclosure. The method of FIG. 3 may be performed by server systems 106,based on information, images, and data received from physicians 102and/or third party providers 104 over electronic network 101.

In one embodiment, step 305 may include receiving patient-specificimaging data of an organ or tissue. In an embodiment, using coronary CTangiography or other imaging technique, the coronary arteries and themuscle of the heart, the myocardium, may both be imaged.

At step 310, a model of one or more vessels that supply blood to atleast a portion of the organ or tissue may be generated based on thepatient-specific imaging data. Techniques disclosed herein may extractthe geometry of the epicardial coronary arteries from the coronary CTangiography data, and append a theoretical model representing the smallarteries and arterioles that cannot be imaged in vivo. The theoreticalmodel may be based on prior patient data, and/or may be selected basedon the patient-specific imaging data.

At step 315, a blood supply may be determined based on the model of oneor more vessels. For example, the total volume of the coronary arterialtree may be computed. The above-mentioned theoretical model mayrepresent the arteries and arterioles which are smaller than a knownimaging threshold for the imaging device used to obtain patient-specificimaging data, and may be included in the determination of blood supply.Blood flow through the coronary arteries may be determined, and may beassumed to be related to the total coronary volume to the ¾ power,although another mathematical relationship may be used.

At steps 320 and 325, an organ or tissue demand may be determined. Atstep 320, for example, demand may be determined based on a mass orvolume of the at least a portion of the organ or tissue. This may be theportion of the organ or tissue which is supplied with blood by thevessels corresponding to the model determined above. For example, thedetermined flow through the coronary arteries may be assumed to berelated to the myocardial mass or volume to the ¾ power, although theexact power or mathematical relationship used may vary. Thus, forexample, the ratio of coronary volume to myocardial mass may be used todetermine a mismatch between coronary supply and myocardial demandindicative of disease or a small caliber coronary arterial tree relativeto the tissue mass that needs to be perfused.

Using either or both steps 320 and 325, the demand for blood of at leasta portion of an organ or tissue may be determined. As discussed above,at step 320, demand for an organ or tissue may be determined based on amass or volume of at least a portion of an organ or tissue. At step 325,the organ or tissue demand, such as the demand of at least a portion ofthe myocardium, may be determined based on perfusion imaging. At step330, and as will be further disclosed in other embodiments presentedherein, the patient may be evaluated based on a comparison of the bloodsupply determined at step 315, and the organ or tissue demand determinedat steps 320 and/or 325. For example, the ratio of blood supply tocorresponding organ or tissue blood demand may indicate a presence orlack of ischemia.

This approach is not necessarily limited to the coronary arteries, or toCT imaging. This may be applied to other organs and tissues, e.g. bloodflow to the brain, kidneys, liver, legs, arms, etc. Imaging techniquesmay vary. For example, the anatomic data to extract an arterial modelmay be obtained using 2D conventional angiography, 3D rotationalangiography, magnetic resonance imaging, or 2D or 3D ultrasound imaging.Organ volume may be obtained using magnetic resonance imaging, or 2D or3D ultrasound imaging, for example. Organ or tissue demand may bedefined from organ or tissue volume, or organ or tissue mass (which maybe computed using the volume data and a measured or assumed organ ortissue density). Organ or tissue demand may also be assessed directlyusing perfusion imaging from CT, MR, PET, or SPECT, or indirectly fromCT, MRI or echocardiographic wall motion data using a model relatingcardiac dynamics and work to blood flow demand.

FIG. 4 is a block diagram of an exemplary method of comparing apatient-specific relationship between arterial blood supply and organ ortissue blood demand to that of a population of prior patients, accordingto an exemplary embodiment of the present disclosure. The method of FIG.4 may be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 101.

In one embodiment, as shown in step 405, a three-dimensionalpatient-specific arterial or anatomic model may be extracted frompatient-specific imaging data, such as imaging data from an imagingdevice. At step 410, a second model of arteries not included in thethree-dimensional patient-specific arterial model may be generated. Forexample, a model of the arteries beyond the limits of the imagingresolution of the imaging device may be generated. The model may bedetermined using branching laws originating from the terminal vesselsextracted from the image data, or by generating vessels to fit withinthe boundaries of the supplied tissue extracted from the imaging data.At step 415, a volume or mass of the relevant tissue of the organsupplied may also be extracted from imaging data. For example, a volumeor mass of the tissue or organ supplied by the three-dimensionalpatient-specific arterial model and the second model may be extracted.The volume or mass may be extracted using image processing methods wherethe organ surfaces are extracted. At step 420, the volume or mass of thetissue or organ may be used to determine a tissue or organ demand. Thismay be done by relating the mass or volume of the tissue or organ to aphysiologic parameter using form-function relations. In one embodiment,total coronary artery blood flow is related to myocardial mass todetermine demand of the heart for blood. At step 425, a measure of theblood supply to the tissue or organ may be generated based on thethree-dimensional patient-specific arterial model and the second model.This may be performed by segmenting the (inner) luminal surface of theblood vessel, computing the luminal volume and relating flow to thecalculated volume. At step 430, a relationship between the tissue ororgan demand and the measure of the blood supply may be determined. Forexample, the ratio of coronary arterial lumen volume to myocardial massmay be calculated. At step 435, the relationship may be compared with apopulation of prior patients for patient evaluation purposes. Forexample, a relationship between a measure of the supplying arteries to ameasure of the organ demanding blood may be calculated, reported, andcompared to a normal reference value derived from a population of priorpatients using statistical methods or machine learning. This comparisonof the derived metric from the individual to the expected value from apopulation may then be used clinically to diagnose disease in theindividual patient. The ratio of coronary arterial lumen volume tomyocardial mass may be predictive of limitations in coronary arteryblood flow to the heart muscle, which may for example cause chest pain.

Techniques presented herein may use metrics related to a measure of thecapacity of the supplying arteries to a measure of the organ demandingblood to refine the physiologic boundary conditions for an individualpatient for use in patient-specific modeling of blood flow. For example,the ratio of vascular volume to organ mass could be calculated for anindividual patient, compared to data from a population of patients, andused to increase or decrease the resistance to flow under baseline,hyperemic, or exercise conditions. This could be applied to thecalculation of noninvasive fractional flow reserve or coronary flowreserve to improve the accuracy of these methods for an individualpatient. For example, in one embodiment, machine learning methods couldbe used together with information on the coronary artery lumen volume tomyocardial mass ratio and measured FFR values in different patients toidentify how the resistance boundary conditions could be adjusted toimprove the accuracy of predictions of computed FFR.

FIG. 5 is a block diagram of an exemplary method of determining arelationship between arterial blood supply and organ or tissue blooddemand, according to an exemplary embodiment of the present disclosure.The method of FIG. 5 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

At step 505, a first patient-specific anatomic model may be received.The model may correspond to arteries of a patient, and may be receivedfrom an imaging device, or an electronic storage device (e.g., a harddrive, network drive, etc.). At step 510, a second patient-specificmodel may be generated for vessels not included in the firstpatient-specific anatomic model. For example, the secondpatient-specific model may include small blood vessels not observable inthe image due to the limits of image resolution, image quality orlimitations of the data collection technique.

At step 515, one or more patient-specific anatomic models of tissue oran organ supplied by the arteries of the first and secondpatient-specific models may be received or generated. The one or moremodels may be received from an electronic storage device (e.g., a harddrive, network drive, etc.).

Similar to techniques presented above, at step 520, the arterial supplymay be determined based on the first and second patient specific models.At step 525, organ or tissue demand may be determined based on thepatient-specific anatomic model of the organ or tissue. Optionally, theorgan or tissue mass may be calculated by measuring or assuming a tissuedensity and multiplying it by the tissue/organ volume. Alternatively,tissue or organ demand may be computed using methods described above.

At step 530, a relationship between arterial supply and organ demand maybe determined based on a metric. In an embodiment, this metric could bethe ratio of the volume of the first patient-specific model to thevolume or mass of the tissue/organ for the patient, or the ratio of thesum of the first and second patient-specific volumes to the volume ormass of the tissue/organ for that same patient.

At step 535, information may be provided on one or more parametersdescribing the relationship between arterial supply and tissue/organdemand. This information may be displayed to a user through a report,visual display or written to an electronic storage device (e.g., harddisk, network drive, cloud storage, smart phone, tablet, etc.).

In some embodiments of techniques described herein, the supply-to-demandmetric(s) computed for an individual patient above may be compared todata from a population of patients to provide additional information asto whether the patient data is within the normal range for anappropriate demographic. In general, the normal supply-to-demandmetric(s) may also depend on patient characteristics such as age,gender, blood pressure etc. This relationship can be inferred from datathat relates all of these characteristics, including thesupply-to-demand metrics, to whether the patient is healthy or diseased.

In some embodiments of techniques described herein, the supply-to-demandmetric(s) computed for an individual patient above may be utilized toupdate the physiologic model for that individual patient and/or computeblood flow and pressure, total or regional tissue perfusion, FractionalFlow Reserve (FFR), Coronary Flow Reserve (CFR), Index ofMicrocirculatory Resistance (IMR), territory at risk, plaque rupturerisk, and/or plaque stress. Machine learning methods may be used tolearn how the supply-to-demand metric(s) could be factored into boundaryconditions assigned to compute coronary flow and pressure. For example,values of the supply-to-demand metrics that indicate that blood flow istoo large to compute FFR accurately, may be used to change the boundaryconditions in the calculation to decrease the flow. This data could beused in conjunction with machine-learning methods to augment thepredictive capability of those methods. For example, a particularsupply-to-demand ratio may be indicative of a certain resistance toflow, as will be discussed further below. The resistance may be used toconfigure a patient-specific model which may be used to simulate bloodflow in a patient's organs and/or tissues.

FIG. 6 is a block diagram of an exemplary method of determining arelationship between arterial blood supply and organ or tissue blooddemand to update a simulation of blood flow and pressure, according toan exemplary embodiment of the present disclosure. The method of FIG. 6may be performed by server systems 106, based on information, images,and data received from physicians 102 and/or third party providers 104over electronic network 101.

At step 605, one or more patient-specific anatomical models may becreated from patient-specific images. Techniques presented herein mayconstruct the patient-specific anatomic model from two-dimensional (e.g.coronary angiography, biplane angiography) or three-dimensional (e.g. 3Drotational angiography, coronary computed tomographic angiography(CCTA), magnetic resonance angiography (MRA)) model. This step mayinclude methods to directly segment the image data and create apatient-specific three-dimensional anatomic model of the patient'sarteries, or may involve modifying a previously-constructed “generic”model to customize it for that patient and create a patient-specificmodel. In either case, the patient-specific anatomic model may includesome or all information related to the arteries of interest, includingthe length of each segment, diameter along the length of a segment (orany other geometrical description of the segment), branching patterns,presence of disease and/or characteristics of disease includingcomposition of atherosclerotic plaques. The representation of the modelmay be defined by a surface enclosing a three-dimensional volume, aone-dimensional model where the centerline of the vessels is definedtogether with cross-sectional area information along the length, orcould be an implicit representation of the vessel surface. The anatomicmodel may represent many different kinds of anatomy, such as coronaryarteries, peripheral arteries, cerebral arteries, visceral arteries,hepatic vessels, renal arteries, etc. The model may also be receivedprior to using the methods and systems described herein.

At step 610, a model of the arterial tree beyond the anatomic modeldiscussed above may be created. In an embodiment, an anatomic model ofthe coronary arteries may be created downstream of the outlets of themodel created above based on the theoretical anatomy of the coronaryarteries by, for example, using data from the literature on coronaryartery branching patterns. Alternatively, this model could also use themeasured organ volume and boundaries to constrain the generated networkof vessels as described in U.S. Pat. Nos. 8,386,188 and 8,315,814, bothof which are incorporated herein by reference.

At step 615, a patient-specific anatomic model of the tissue or organsupplied by the arteries of the first and second patient-specific modelsmay be generated. In one embodiment, this is a model of the entireheart, the individual chamber tissue volumes, or the left ventriclemyocardium extracted from CCTA imaging data. This model may be used toestimate organ demand for blood.

At step 620, a metric relating vascular supply to organ demand may bedefined or determined. In one embodiment, this metric is the ratio ofthe epicardial coronary artery volume (calculated in the step above) orthe total coronary arterial volume (calculated in steps above) tomyocardial mass, i.e., volume/mass. A low volume to mass ratio may beassociated with presence of ischemia, whereas a higher volume to massratio may be associated with absence of ischemia. For example, a ratioof volume/mass, using units mm³/g, of 30 or above may be associated withabsence of ischemia. A ratio of below 30, and especially below 15, maybe associated with presence of ischemia. As another example, a ratioabove 30 may be classified as non-ischemic, 30-15 as moderatelyischemic, and below 15 as ischemic. The specific thresholds, number andtype of categorizations may vary. This metric may be determined in asimilar manner using any vessels, organs or tissue.

The supply-to-demand metrics may also be useful in predicting coronaryflow reserve (CFR). For example, it is expected that low values of CFRwould be observed in patients with low values of supply-to-demandmetrics. A ratio of volume/mass, using units mm³/g, of below 30, andespecially below 15, may be associated with low coronary flow reserve.

Another step of techniques presented herein may be to report theabove-calculated metric to a patient, physician or health care provider.

At step 625, a simulation of blood flow and pressure may be updatedusing the above calculated metric in comparison to population-based datato refine the physiologic model. This may be performed by adjustingvalues for microvascular resistance based on the above calculatedmetric.

While one embodiment is related to more accurately computing blood flowand pressure in the human coronary arteries, other embodiments mayinclude computing blood flow and pressure in the extracranial andintracranial cerebral arteries, the lower extremity arteries includingthe iliac, superficial femoral, common femoral, tibial, popliteal,peroneal, pedal arteries in patients with peripheral arterial disease,the renal arteries, the mesenteric arteries, and/or other vascular beds.This may be used to improve the methods described in U.S. Pat. Nos.8,386,188 and 8,315,814, incorporated by reference in their entirety,which relate to simulating perfusion in the heart and brain,respectively.

In addition, techniques presented herein may cause an improvedcalculation of blood flow and pressure which could improve theprediction of plaque rupture as, for example, described in U.S. Pat. No.8,311,748, which is incorporated by reference in its entirety. Thesetechniques may result in more accurate predictions of baselineconditions that could then be used in treatment planning for example asdescribed in U.S. Pat. Nos. 8,157,742, 8,594,950, and 8,734,357, whichare incorporated by reference in their entirety. This method can also berepeated with/without vasodilating drugs to assess the dilatory capacityof the epicardial arteries/microvasculature.

FIG. 7 is a block diagram of an exemplary method of determining acoronary supply and myocardial demand, according to an exemplaryembodiment. The method of FIG. 7 includes steps of many of theprior-described embodiments, and applies those steps specifically to theheart and coronary arteries. Any of the details of corresponding stepsin earlier-described embodiments may be used in the FIG. 7 method. Themethod of FIG. 7 may be performed by server systems 106, based oninformation, images, and data received from physicians 102 and/or thirdparty providers 104 over electronic network 101.

At step 705, patient-specific image data obtained using an imagingdevice may be received, wherein at least a portion of thepatient-specific image data corresponds to at least a portion of apatient's heart. At step 710, a first patient-specific model of vesselgeometry of at least a first portion of a coronary artery may bereceived, wherein the model is based on the received patient-specificimage data. At step 715, a second patient-specific model representing atleast a second portion of a coronary artery may be received, wherein thesecond portion is associated with and smaller than the first portion ofthe coronary artery, and wherein the second portion of the coronaryartery is below an imaging threshold of the imaging device. At step 720,a coronary supply based on the first patient-specific model and thesecond patient-specific model may be determined. At step 725, at least aportion of the myocardium corresponding to the coronary artery may bedetermined. At step 730, a myocardial demand based on a mass or volumeof the portion of the myocardium, or based on perfusion imaging of theportion of the myocardium, may be determined. At step 735, arelationship between the coronary supply and myocardial demand may bedetermined.

For exemplary purposes, multiple embodiments are described herein. Anyof the details of any steps of any embodiment described herein may beused with similar steps of other embodiments.

Techniques presented herein describe methods which may determine arelationship between blood supply to an organ or tissue, and blooddemand from that organ or tissue. These techniques provide significantinsight into the overall disease burden of patients with atherosclerosiswhich would have prognostic value. Novel approaches described hereininclude determining a relationship between blood supply and blood demandin relationship to a given organ or tissue. Such a determination may bemade for patients under resting, hyperemic and/or exercise conditions.These techniques may apply to the coronary arteries, but also tosimulations of blood flow and pressure in any arterial tree including,but not limited to, the carotid, cerebral, renal, and lower extremityarteries.

Techniques presented herein may calculate the ratio of blood flow andresistance based on vascular volume to that based on myocardial volumeor mass, and may be implemented and included in an FFRCT platform.Methods to compute and display the resting flow mismatch or update theset of physiologic conditions and boundary conditions of the patientusing this data may also be performed.

Other embodiments of the invention will be apparent to those skilled inthe art from consideration of the specification and practice of theinvention disclosed herein. Numerous embodiments are discussed herein,which may be used in various combinations with each other. It isintended that the specification and examples be considered as exemplaryonly, with a true scope and spirit of the invention being indicated bythe following claims.

1-20. (canceled)
 21. A computer-implemented method of determining a blood supply and a blood demand, the method comprising: receiving a first portion of a patient-specific model of vessel geometry, wherein the first portion of the patient-specific model is based on patient-specific image data; generating a second portion of the patient-specific model of vessel geometry, wherein the second portion of the patient-specific model is based on data not shown in the patient-specific image data; determining an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the vessel geometry based on the first portion and the second portion of the patient-specific model; and evaluating the patient based the amount of blood demanded by the tissue or the organ.
 22. The method of claim 21, wherein evaluating the patient includes determining, based on the evaluation, a disease state of the patient.
 23. The method of claim 21, wherein the second portion of the patient-specific model is based at least in part on population-based data.
 24. The method of claim 21, further comprising: generating a simulation of blood flow identifying a relationship between the patient's vessel supply and the patient's tissue or organ demand.
 25. The method of claim 24, further comprising: modifying, based on the evaluation, at least one parameter of the simulation of blood flow through the patient's vessel geometry.
 26. The method of claim 21, wherein the second portion of the patient-specific model is a model of one or more vessels downstream of a vessel in the vessel geometry of the first portion of the patient-specific model.
 27. The method of claim 21, wherein evaluating the patient includes predicting a location of a plaque rupture in the vessel geometry.
 28. A system for image processing to determine a blood supply and a blood demand, the system comprising: at least one data storage device storing instructions for determining the blood supply and the blood demand; and at least one processor configured to execute the instructions to perform operations comprising: receiving a first portion of a patient-specific model of vessel geometry, wherein the first portion of the patient-specific model is based on patient-specific image data; generating a second portion of the patient-specific model of vessel geometry, wherein the second portion of the patient-specific model is based on data not shown in the patient-specific image data; determining an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the vessel geometry based on the first portion and the second portion of the patient-specific model; and evaluating the patient based the amount of blood demanded by the tissue or the organ.
 29. The system of claim 28, wherein evaluating the patient includes determining, based on the evaluation, a disease state of the patient.
 30. The system of claim 28, wherein the second portion of the patient-specific model is based at least in part on population-based data.
 31. The system of claim 28, wherein the operations further comprise: generating a simulation of blood flow identifying a relationship between the patient's vessel supply and the patient's tissue or organ demand.
 32. The system of claim 31, wherein the operations further comprise: modifying, based on the evaluation, at least one parameter of the simulation of blood flow through the patient's vessel geometry.
 33. The system of claim 28, wherein the second portion of the patient-specific model is a model of one or more vessels downstream of a vessel in the vessel geometry of the first portion of the patient-specific model.
 34. The system of claim 28, wherein evaluating the patient includes predicting a location of a plaque rupture in the vessel geometry.
 35. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method of determining a blood supply and a blood demand, the method comprising: receiving a first portion of a patient-specific model of vessel geometry, wherein the first portion of the patient-specific model is based on patient-specific image data; generating a second portion of the patient-specific model of vessel geometry, wherein the second portion of the patient-specific model is based on data not shown in the patient-specific image data; determining an amount of blood demanded by at least a portion of a tissue or organ receiving blood from the vessel geometry based on the first portion and the second portion of the patient-specific model; and evaluating the patient based the amount of blood demanded by the tissue or the organ.
 36. The non-transitory computer readable medium of claim 35, wherein evaluating the patient includes determining, based on the evaluation, a disease state of the patient.
 37. The non-transitory computer readable medium of claim 35, wherein the second portion of the patient-specific model is based at least in part on population-based data.
 38. The non-transitory computer readable medium of claim 35, wherein the method further comprises: generating a simulation of blood flow identifying a relationship between the patient's vessel supply and the patient's tissue or organ demand.
 39. The non-transitory computer readable medium of claim 38, wherein the method further comprises: modifying, based on the evaluation, at least one parameter of the simulation of blood flow through the patient's vessel geometry.
 40. The non-transitory computer readable medium of claim 35, wherein the second portion of the patient-specific model is a model of one or more vessels downstream of a vessel in the vessel geometry of the first portion of the patient-specific model. 